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@@ -36,9 +36,27 @@ Testset 1 consists of parallel sentences in Ladin and Italian. The dataset conta
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  If you use this dataset, please cite the following paper:
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  ```bibtex
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- @article{Frontull:Moser:2024,
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- title={Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin},
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- author={Frontull, Samuel and Moser, Georg},
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- journal={arXiv preprint arXiv:2407.08819},
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- year={2024}
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  }
 
 
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  If you use this dataset, please cite the following paper:
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  ```bibtex
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+ @inproceedings{frontull-moser-2024-rule,
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+ title = "Rule-Based, Neural and {LLM} Back-Translation: Comparative Insights from a Variant of {L}adin",
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+ author = "Frontull, Samuel and
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+ Moser, Georg",
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+ editor = "Ojha, Atul Kr. and
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+ Liu, Chao-hong and
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+ Vylomova, Ekaterina and
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+ Pirinen, Flammie and
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+ Abbott, Jade and
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+ Washington, Jonathan and
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+ Oco, Nathaniel and
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+ Malykh, Valentin and
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+ Logacheva, Varvara and
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+ Zhao, Xiaobing",
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+ booktitle = "Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
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+ month = aug,
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+ year = "2024",
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+ address = "Bangkok, Thailand",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2024.loresmt-1.13",
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+ pages = "128--138",
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+ abstract = "This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.",
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  }
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+ ```